A multidisciplinary faculty committee designed a curriculum to shape biomedical graduate students into researchers with a high commitment to professionalism and social responsibility and to provide students with tools to navigate complex, rapidly evolving academic and societal environments with a strong ethical commitment. The curriculum used problem-based learning (PBL), because it is active and learner-centred and focuses on skill and process development. Two courses were developed: Scientific Professionalism: Scientific Integrity addressed discipline-specific and broad professional norms and obligations for (...) the ethical practice of science and responsible conduct of research (RCR). Scientific Professionalism: Bioethics and Social Responsibility focused on current ethical and bioethical issues within the scientific profession, and implications of research for society. Each small-group session examined case scenarios that included: (1) learning objectives for professional norms and obligations; (2) key ethical issues and philosophies within each topic area; (3) one or more of the RCR instructional areas; and (4) at least one type of moral reflection. Cases emphasised professional standards, obligations and underlying philosophies for the ethical practice of science, competing interests of stakeholders and oversight of science (internal and external). To our knowledge, this is the first use of a longitudinal, multi-semester PBL course to teach scientific integrity and professionalism. Both faculty and students endorsed the active learning approach for these topics, in contrast to a compliance-based approach that emphasises learning rules and regulations. (shrink)

The paper describes an innovative structured workshop methodology in garden-based-learning called “Nature in Your Face” aimed at provoking a change in citizens behavior and engagement as a consequence of the emotional activation in response to disruptive artistic messages. The methodology challenges the assumption that the change needed to meet the carbon targets can be reached with incremental, non-invasive behavior engineering techniques such as nudging or gamification. Instead, it explores the potential of disruptive communication to push citizens out of (...) their comfort zones and into creative modes of re-creating futures. This is done by confronting us with consequences of consumption by means of art and eco-visualizations. The aim being to achieve awareness, mental flexibility, and spurring debate. Thus, we invite them to react – and act upon these reactions by communicating their feelings or thoughts. This is used as an entrance point for broader and/or deeper engagement in a structured three-step methodology; Framing: A disruptive element is introduced into the local environment. This triggers an emotional reaction, which then is taken up in a process of framing the problem and working with solutions. Twisting: in a guided process, the initial energy from the emotional confrontation is twisted into a creative exploration of innovative solutions, from the perspective of the children. Using: The last stage is using the developed solutions in the local social system. The methodology is being applied in cooperation with a primary school, and is iteratively adjusted and evaluated in a formative, action-research based approach scenario. NiYF is to stimulate societal engagement through deliberately confronting stakeholders with unexpected and potentially upsetting appearances of nature, climate effects, or carbon-neutral lifestyle scenarios. We have verified the practical usefulness and potential of the methodology and found that NiYF triggers societal engagement and transition, negotiating responsibilities and unlocking action potentials. We also found that eco-anxiety, denial, self-efficacy and cognitive dissonance form children’s understanding of plastic. The project marks a paradigm shift from creating knowledge to using knowledge to create action, and a focus on learning from evaluating and adapting the approach in the field. (shrink)

Background This study used a scenario- and discussion-based approach to teach preclinical medical students the socio-philosophical aspects of psychiatry and qualitatively evaluated the learning outcomes in a medical humanities course in Taiwan. Methods The seminar session focused on three hypothetical psychiatry cases. Students discussed the cases in groups and were guided by facilitators from multiple disciplines and professions. At the end of the semester, students submitted a narrative report comprising their reflections on the cases and discussions. The (...) authors utilized content analysis to categorize students’ narratives into three facets, namely, the philosophical, social and individual. Results In total 163 preclinical medical students participated in the class; 150 of them mentioned the scenario-based lesson in their reports; 33.3% of these reports discussed the case at the philosophical dimension (n = 50), 45.3% at the social dimension (n = 68), and 26.6% at the individual dimension (n = 40). Four major themes emerged: (1) a psychiatric diagnosis has far-reaching consequences for an individual’s life, (2) the social structure affects how patients experience psychiatric disorders, (3) students related personal experience or those of friends and family to understand psychiatric disorders, and (4) medical humanities are of particular importance in psychiatric education. Conclusions This study demonstrated that the scenario-based discussions led by a multidisciplinary team of facilitators can benefit medical students with limited clinical experience to contemplate the socio-philosophical aspects of psychiatry. The authors suggest that this pedagogical model during preclinical education should be encouraged. (shrink)

Neurodegenerative Parkinson's Disease is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite and intelligent Motor Assessment Tests, produced (...) within the i-PROGNOSIS European project, are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 and Stage 3 produced from their interaction with PGS/iMAT are analyzed. Five feature vector scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor, Support Vector Machines, and Random Forest, to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation method is adopted for testing the classification performance. The experimental results show that a high classification accuracy is achieved from both data sources, justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions. (shrink)

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural (...) networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft. (shrink)

The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. (...) Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model. (shrink)

In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. In terms of moving target detection, the traditional detection algorithms are analysed theoretically as well as implemented algorithmically, based on which a fusion algorithm of four interframe difference method and background averaging method is proposed for the shortcomings of interframe difference method and background difference method. The fusion algorithm uses the learning rate (...) to update the background in real time and combines morphological processing to correct the foreground, which can effectively cope with the slow change of the background. According to the requirements of real time, accuracy, and occupying less video memory space in intelligent video surveillance systems, this paper improves the streamlined version of the algorithm. The experimental results show that the improved multitarget tracking algorithm effectively improves the Kalman filter-based algorithm to meet the real-time and accuracy requirements in intelligent video surveillance scenarios. (shrink)

Nurses are experiencing new ethical issues as a result of global developments and changes in health care. With health care becoming increasingly sophisticated, and countries facing challenges of graying population, ethical issues involved in health care are bound to expand in quantity and in depth. Blended learning rather as a combination of multiple delivery media designed to promote meaningful learning. Specifically, this study was focused on two questions: (1) the students’ satisfaction and attitudes as members of a (...) class='Hi'>scenario-based learning process in a blended learning environment; (2) the relationship between students’ satisfaction ratings of nursing ethics course and their attitudes in the blended learning environment. In total, 99 senior undergraduate nursing students currently studying at a public nursing college in Taiwan were invited to participate in this study. A cross-sectional survey design was adopted in this study. The participants were asked to fill out two Likert-scale questionnaire surveys: CAAS (Case Analysis Attitude Scale), and BLSS (Blended Learning Satisfaction Scale). The results showed what students felt about their blended learning experiences — mostly items ranged from 3.27—3.76 (the highest score is 5). Another self-assessment of scenario analysis instrument revealed the mean scores ranged from 2.87—4.19. Nearly 57.8% of the participants rated the course ‘extremely helpful’ or ‘very helpful.’ This study showed statistically significant correlations (r = 0.43) between students’ satisfaction with blended learning and case analysis attitudes. In addition, results testified to a potential of the blended learning model proposed in this study to bridge the gap between students and instructors and the one between students and their peers, which are typical of blended learning, and to create meaningful learning by employing blended pedagogical consideration in the course design. The use of scenario instruction enables students to develop critical analysis and problem solving skills through active learning and social exchange of ideas. (shrink)

This study presents the first assessment of how an approach based on meeting fundamental human needs can assist regional planning. It uses the Human-scale Development methodology, based on fundamental human needs as a theoretical and methodological framework for scenario building. It offers a structured approach on how non-monetary values and practices (i.e. satisfiers or ways to satisfy needs) can help to open up the planning process, highlighting a regional conflict. The study presents three dimensions of needs to (...) address planning challenges. The data is taken from a case study of a deliberative process for regional planning in Western Europe. The relevance lies in the ways we can learn from individual values and practices, that when shared with others, (1) help to diagnose behaviours and trends toward environmentalism; (2) foster listening and understanding of people's sameness and differences that reduce conflict; and (3) provide a structured tool which predicts society dynamics and develops integrated solutions that facilitate sustainable regional development. (shrink)

How do we prepare engineering students to make ethical and responsible decisions in their professional work? This paper presents an approach that enhances engineering students’ engagement with ethical reasoning by simulating decision-making in a complex scenario. The approach has two principal inspirations. The first is Anthony Weston’s scenario-based teaching. Weston’s concept of a scenario is a situation that changes in response to choices made by participants, according to an inner logic. Scenarios can dynamically explore open-ended complex (...) problems without imposing predetermined results, allowing students to apply their ethical reasoning in a more realistic fashion. The second inspiration is Thi Nguyen’s theory of games. According to Nguyen, games are the “art of agency,” meaning that they provide a structure under which some result is agreed to be valuable, along with prescribed ways of achieving that result, in a relatively low-stakes and temporary social arrangement. By playing many games, Nguyen argues, we can develop a “library of agency,” that is to say, a set of values and means to achieving them that can inform how we make high-stakes decisions in real life. Engaging with carefully designed game scenarios, then, can enable students to practice making ethical decisions within a structured framework, which unfolds in response to their choices, while their decisions are driven by some defined goal. This roleplaying game–based approach thus allows students to build their own ethical libraries of agency, in preparation for professional decision-making. The approach is illustrated using a specific example recently deployed in a seminar on engineered living materials (building materials that incorporate living tissues). The scenario uses techniques from tabletop roleplaying games (e.g. Dungeons & Dragons) for outcome resolution, adventure design, and character creation. Through the game, students practice making difficult decisions in a humanitarian context. (shrink)

Fall detection is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers, developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: an analysis of the event detection stage, comparing several alternatives, an evaluation of features to extract for each detected event and an appraisal of up to 6 different (...) clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices. (shrink)

The study aims to explore the roles of Massive Open Online Courses based on deep learning in college students’ English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and (...) does not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of “teaching students according to their personalities” to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers’ deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students. (shrink)

In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then (...) extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. (shrink)

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, (...) to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. (shrink)

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy (...) through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant (...) increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Background Ethical dilemmas that arise in the clinical setting often require the collaboration of multiple disciplines to be resolved. However, medical and nursing curricula do not prioritize communication among disciplines regarding this issue. A common teaching strategy, problem-based learning, could be used to enhance communication among disciplines. Therefore, a university in southern Taiwan developed an interprofessional ethics education program based on problem-based learning strategies. This study described tutors’ experience teaching in this program. Aim To explore (...) the phenomenon of teaching and learning in interprofessional ethics education for medical and nursing students from the perspectives of tutors. Design Phenomenological qualitative research. Methods Medical and nursing students completed a 6-week interprofessional ethics education program moderated by either physician or nurse tutors. At the conclusion of the ethics education program, all 14 tutors were invited to participate in focus group interviews. Among them, six tutors (three nursing tutors and three physician tutors) participated in additional individual interviews. All of the contents from the focus group interviews and individual interviews were recorded and transcribed. Using the phenomenological approach, the phenomenon of teaching and learning in interprofessional ethics education were generated. Ethical consideration The study was approved by the Institutional Review Board. Findings Three themes emerged from the tutors' teaching perspectives, including the instructor’s motivation to teach, the use of narrative case scenarios, and the emphasis on improving interprofessional ethics communication. Discussion Problem-based learning creates an interprofessional communication platform in interprofessional ethics education. The phenomenon of value convergence between tutors and students, between different students’ professions, and between different students’ professional maturities is observed. Conclusion Problem-based learning is an effective teaching strategy for creating a communication platform for interprofessional ethics education. Ethic curriculum should emphasize motivating instructor, use narrative case scenarios, and focus on interprofessional communication. (shrink)

Conventional methods for fault diagnosis typically require a substantial amount of training data. However, for equipment with high reliability, it is arduous to form a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation. Besides, the generated data have a large number of redundant features which degraded the performance of models. To overcome this, we proposed a feature transfer scenario that transfers knowledge from similar fields to enhance the accuracy of fault diagnosis with small sample. (...) To reduces the redundant information, data were filtered according to manifold consistency. Then, features were extracted based on CNN and feature transfer was conducted. For adequate fitness, the joint adaptation of conditional distribution and marginal distribution was used between the two domains. Minimum structural risk and MMD of adaptation were two indicators weighted for training the model. To test the efficiency of the model, we built an airborne fuel pump testbed, and contributed a new dataset that contained 15 categories of fault data, which serves as the small sample dataset in this research. Then the proposed model was applied in our experimental data. As a result, the fault diagnosis rate increases by 28.6% through our proposed model, which is more precise than other classical methods. The results of feature visualization further demonstrate that the features are more distinguished through the proposed method. All code and data are accessible on my GitHub. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary (...) class='Hi'>learning. The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

Understanding students’ psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students’ normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In addition, a variety of experimental (...) methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive–compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. For the optimization of ant colony algorithm computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized. (shrink)

This book explains the importance to acquire complex problem-solving in today’s job environment. The book describes how to use five problem-centered methods to design training for real-world complex problem-solving skills. The book briefly describes the five methods in the context of the complex problem-solving skills - Problem-based learning (PBL), Project-based learning, Scenario-based learning (SBL), Case-based learning method (CBL), and Simulation-based learning. The book also specifies six research-based guidelines, and (...) how training experts can design a training curriculum that ensures acquiring and accelerating complex problem-solving skills in any complex domain. (shrink)

You can find countless books dedicated to student success and resilience. But what about the faculty? What do we do to help college faculty cultivate their professional and personal growth and resilience? During more than three decades as a teacher and workshop facilitator, Steve Piscitelli noticed that many educators can become isolated from their colleagues and their larger institutional culture. They become "islands" disconnected from the potential power of the teaching and learning community. That isolation can affect teaching efficacy (...) and resilience. To encourage educators to embrace the power of collaboration, Piscitelli outlines the "7Rs" for success: relationships, relevance, resources, rainbows, responsibility, reflection, and resilience. He created thirty-seven scenarios, based on real life issues. Each shows how to apply these seven themes to better adapt to professional and personal challenges educators face. The scenarios raise questions about the classroom, work-life integration, collaboration with coworkers, and interpersonal relationships with supervisors. Every teaching and learning situation-every one-gives us the opportunity to take stock of and strengthen our own resilience level. And we do not have to do it alone. That is the beauty of our calling! And that is the strength of this book. It recognizes the power of a collegial teaching and learning community. Piscitelli supplements the information and scenarios with links to brief thought-provoking videos (approximately sixty seconds each) that encapsulate major themes and discussion points. More information at www.stevepiscitelli.com. I think it's brilliant. I often feel like I'm on an island as a professor. -Ashli Archer, Professor I like how you let people know that even though you have developed a table of contents, they might want to skip around. The flexibility will be very appealing.-Erin Hoag, VP of Financial Services and Strategic Development, Innovative Educators This book will be a welcome resource! -Todd Stanislav, Director, Faculty Center for Teaching and Learning Wow, you are covering some controversial topics! This book is going to be different and invaluable!-Pam Ranallo, Co-Owner, Innovative Educators Your scenario addressing the issue of favoritism vs. jealousy demonstrates amazing insight and ability to address the "hard" topics. Bravo!-Karen Armstrong, Career Counselor I think this is a wonderful idea and have never seen anything along these lines before!-Mary Boone Treuting, Director of Center for Academic Success and the Center for Teaching Excellence. (shrink)

Objective: Mindfulness-based intervention has been proposed to alleviate loneliness and improve social connectedness. Several randomized controlled trials have been conducted to evaluate the effectiveness of MBI. This study aimed to critically evaluate and determine the effectiveness and safety of MBI in alleviating the feeling of loneliness.Methods: We searched Medline, Embase, PsycInfo, Cochrane CENTRAL, and AMED for publications from inception to May 2020. We included RCTs with human subjects who were enrolled in MBI with loneliness as an outcome. The quality (...) of evidence was assessed using Cochrane's Risk of Bias tool and Grading of Recommendations Assessment, Development, and Evaluation. A random-effects model was used for meta-analysis.Results: Out of 92 articles identified, eight studies involving 815 participants were included in this study. Most trials conducted a minimum of 8 weeks of MBI. Most of the trials used UCLA-Loneliness Scale. A pooled analysis combining three trials and compared with wait-list showed significant improvement in loneliness score reduction using the UCLA-R scale with MD of −6.33 [95% confidence interval : −9.39, −3.26]. Subgroup analysis with only two Cognitively-Based Compassion Training trials also showed similar MD of −6.05. The overall quality of evidence was low.Conclusions: Mindfulness intervention with an average length of 8-week duration significantly improved the population's loneliness level with no mental health issue. However, this evidence had a low GRADE level. (shrink)

Research on fairness in machine learning (ML) has been largely focusing on individual and group fairness. With the adoption of ML-based technologies as assistive technology in complex societal transformations or crisis situations on a global scale these existing definitions fail to account for algorithmic fairness transnationally. We propose to complement existing perspectives on algorithmic fairness with a notion of transnational algorithmic fairness and take first steps towards an analytical framework. We exemplify the relevance of a transnational fairness assessment (...) in a case study on a disaster response system using images from online social media. In the presented case, ML systems are used as a support tool in categorizing and classifying images from social media after a disaster event as an almost instantly available source of information for coordinating disaster response. We present an empirical analysis assessing the transnational fairness of the application’s outputs-based on national socio-demographic development indicators as potentially discriminatory attributes. In doing so, the paper combines interdisciplinary perspectives from data analytics, ML, digital media studies and media sociology in order to address fairness beyond the technical system. The case study investigated reflects an embedded perspective of peoples’ everyday media use and social media platforms as the producers of sociality and processing data-with relevance far beyond the case of algorithmic fairness in disaster scenarios. Especially in light of the concentration of artificial intelligence (AI) development in the Global North and a perceived hegemonic constellation, we argue that transnational fairness offers a perspective on global injustices in relation to AI development and application that has the potential to substantiate discussions by identifying gaps in data and technology. These analyses ultimately will enable researchers and policy makers to derive actionable insights that could alleviate existing problems with fair use of AI technology and mitigate risks associated with future developments. (shrink)

The aim of this study was to explore the emergence of skilled behaviours, in the form of actions, cognitions and emotions, between professional state level cricket batters and their lesser skilled counterparts. Twenty-two male cricket batsmen (n = 6 state level; n = 8 amateur grade club level, n = 8 junior state representative level) participated in a game scenario training session against right arm pace bowlers (n = 6 amateur senior club). The batsmen were tasked with scoring as (...) many runs as possible during a simulated limited-overs game. The actions, cognitions and emotions of each batsmen were recorded in situ with findings showing differences between state level players and those lesser skilled. State level batsmen played more scoring shots and scored more runs, underpinned by superior bat-ball contact and technical efficiency. Furthermore, the state player’s cognitive evaluations of their own performance differed from junior batters, with more reported strategies based on an external outcome focus, such as where to score runs, rather than a focus on internal processes, such as making technical changes. State level batsmen also reported lower levels of nervousness compared with junior level batsmen. These results highlight the importance of viewing the emergence of skilled behaviour as multi-faceted, rather than simply the acquisition of superior execution and technical proficiency. (shrink)

Any first step in organisational adaptation starts with individuals’ responses and willingness (or otherwise) to change an aspect of themselves given the transcontextual settings in which they are operating (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018). This research explores the implications for organisational adaptation strategies when Artificial Intelligence (AI) is being embedded into the ecology of the organisation, and when employees have a dominant fixed or growth mindset (Dweck in Mindset: changing the (...) way you think to fulfil your potential. Robinson, London, 2017). Research participants were supplied with a single scenario based in 2030, where—as a result of Artificial Intelligence technology implementation—employees were going to be displaced. Using Torbert’s (Organizational wisdom and executive courage, New Lexington Press, San Francisco, 1998) ‘first, second and third person’ research theory, participants were asked to independently review their thoughts, sense, and image of the future from a fixed mindset position (considered to be the worst case), then from a growth mindset perspective (best case), and then do the same collectively. Five key findings are outlined which support the principle that having a growth mindset is a key component of adaptive capacity and futures literacy. The five key findings conclude that AI adaptation processes need to include compassion and authenticity, embodiment, fundamental needs and motivations, mutual learning and considering what lies beyond the edges of the organisation (Bateson in Small arcs of larger circles: framing through other patterns, Triarchy Press, Axminster, 2018). (shrink)

Research on authentic learning has been predominantly focussed on skills-based training: there is a paucity of research on models of authentic learning available for adaptation in the humanities undergraduate classroom. In this article, I will seek to address this gap by proposing that legal trials are ideal models for designing authentic learning scenarios in undergraduate teaching and learning contexts, with a specific focus on the humanities. First, I discuss why and how the structure of legal (...) trials can produce authentic learning environments. Second, I present an undergraduate classroom project that combined two disciplinary fields – Shakespearean drama and criminal law – in an effort to enhance student learning and engagement. I outline how the authentic learning scenario was implemented and evaluated and, finally, reflect on the barriers, challenges and potentially transformative effect of authentic learning environments on students and educators. This new intervention combines legal studies... (shrink)

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship (...) between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image, is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors. (shrink)

The paper develops a semi-formal model of learning which modifies the traditional paradigm of artificial neural networks, implementing deep learning by means of a key insight borrowed from the works of Marvin Minsky: the so-called Principle of Non-Compromise. The principle provides a learning mechanism which states that conflicts in the processing of data to be integrated are a mark of unreliability or irrelevance; hence, lower-level conflicts should lead to higher-level weight-adjustments. This internal mechanism augments the external mechanism (...) of weight adjustment by back-propagation, which is typical for the standard models of machine learning. The text is structured as follows: (§1) opens the discussion by providing an informal overview of real-world decision-making and learning; (§2) sketches a typology of decision architectures: the individualistic approach of classical decision theory, the general aggregation mechanism of social choice theory, the local aggregation mechanism of agent-based modeling, and the intermediate hierarchical model of Marvin Minsky's “Society of Mind”; (§3) sketches the general outline of ANIMA – a new model of decision-making and learning that borrows insights from Minsky's informal exposition; (§4) is the bulk of the paper; it provides a discussion of a toy exemplification of ANIMA which lets us see the Principle of Non-Compromise at work; (§5) lists some possible scenarios for the evolution of a model of this kind; (§6) is the closing section; it discusses some important differences between the way ANIMA was construed here and the typical formal rendering of learning by means of artificial neural networks and deep learning. (shrink)

In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model, so-called motto-goals should activate a person’s resources through positive affect. (...) It was found that motto-goals are effective with unpleasant duties. Therefore, we tested the hypothesis that motto-goals outperform learning and performance goals in the case of complex problems. A total of N = 123 subject participated in the experiment. In dependence of their goal condition, subjects developed a personal motto, learning, or performance goal. This goal was adapted for the computer-simulated complex scenario Tailorshop, where subjects worked as managers in a small fictional company. Other than expected, there was no main effect of goal condition for the management performance. An unexpected gender effect revealed better performance for men than women, pointing to a potential stereotype threat. As hypothesized, motto goals led to higher positive and lower negative affect than the other two goal types. Even though positive affect decreased and negative affect increased in all three groups during Tailorshop completion, participants with motto goals reported the lowest rates of negative affect. Exploratory analyses investigated the role of affect in complex problem solving via mediational analyses and the influence of goal type on perceived goal attainment. (shrink)

As the development of artificial intelligence technology, the deep-learning -based Virtual Reality technology, and DL technology are applied in human-computer interaction, and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body (...) motions based on the convolutional neural network algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically. (shrink)

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning (...) framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task. (shrink)

Ethics have taken a center stage in business curriculum development over the past 5 years. Sustainable business practices are an important issue when it comes to adequately educating the next generation of marketing professionals. A variety of approaches in how to achieve such goals have been proposed as ideal methodologies. This paper presents a case study on curriculum development for a study abroad trip in Cambodia for marketing students. Furthermore, this article represents one method to incorporate the role of NGOs (...) in international business into business ethics courses. Results show that values-based curriculum serve as an appropriate learning pedagogy for the advancement of ethics in business educational scenarios. Through the use of values-based format, results demonstrate that when students are presented in constructive ethical situations it induces critical self-reflection necessary for more effective ethics education. Furthermore, with the additional cross-cultural component of the research design, students were also exposed to ethics as a component of culture. The current study extends both the pedagogy and ontological development and application of ethics education, specifically, the values-based curriculum providing a pragmatic approach to ethics teachings, and also presents an empirical study of how to integrate NGOs into international business educational platforms. (shrink)

Higher education has become more student-centered as the Bologna process assigns students more time to study and research. Online teaching has been needed during the pandemic, which can be challenging regarding didactic and assessment. This paper analyzes project-based learning (PBL) as a form of teaching and assessing students in a bioethics course on reproductive ethics. The team project was the final assessment of the Faculty of Bioethics core curriculum course, "Bioethics, Technology and Procreation,” offered to two student groups (...) in the 2019–2020 school year. The analysis of the results of PBL is descriptive qualitative with semi-quantitative data from student feedback. Forty students were presented with a detailed methodology of team projects and were encouraged to form teams of 3 to 5. They need to develop a team project with creativity and pastoral sensibility that will communicate the content assimilated in class to a selected target audience, with adequate means to measure the impact of their project on their target. Thirty-eight students formed ten teams and presented ten projects. Each team had 2–4 encounters with the professor and 6–10 encounters among team members. Six of the ten projects were categorized as didactic, three mediatic, one didactic-mediatic and 0 artistic. The grades of this final assignment ranged from 7 to 10, with an average of 8.7 out of 10. The survey feedback demonstrated high satisfaction, as students discovered new values in teamwork and pastoral applications. However, this innovation can be more time-consuming for the professor and the students, requiring more significant time management, teamwork and communication competency. (shrink)

Causal and predictive learning research often employs intuitive and familiar hypothetical scenarios to facilitate learning novel relationships. The allergist task, in which participants are asked to diagnose the allergies of a fictitious patient, is one example of this. In such studies, it is common practice to ask participants to ignore their existing knowledge of the scenario and make judgments based only on the relationships presented within the experiment. Causal judgments appear to be sensitive to instructions that (...) modify assumptions about the scenario. However, the extent to which prior knowledge continues to affect competition for associative learning, even after participants are instructed to disregard it, is unknown. To answer this, we created a cue competition design that capitalized on prevailing beliefs about the allergenic properties of various foods. High and low allergenic foods were paired with foods moderately associated with allergy to create two compounds; high + moderate and low + moderate. We expected high allergenic foods to produce greater competition for associative memory than low allergenic foods. High allergenic foods may affect learning either because they generate a strong memory of allergy or because they are more salient in the context of the task. We therefore also manipulated the consistency of the high allergenic cue-outcome relationship with prior beliefs about the nature of the allergies. A high allergenic food that is paired with an inconsistent allergenic outcome should generate more prediction error and thus more competition for learning, than one that is consistent with prior beliefs. Participants were instructed to either use or ignore their knowledge of food allergies to complete the task. We found that while participants were able to set aside their prior knowledge when making causal judgments about the foods in question, associative memory was weaker for the cues paired with highly allergenic foods than cues paired with low allergenic foods regardless of instructions. The consistency manipulation had little effect on this result, suggesting that the effects in associative memory are most likely driven by selective attention to highly allergenic cues. This has implications for theories of causal learning as well as the way causal learning tasks are designed. (shrink)

This chapter contains sections titled: Introduction Feminist Turn Psyche and Soma Embodiment and Knowing The Body and Cognition Learning between the Biological and Symbolic Implications for Education Acknowledgement References.

If I lose my key in Canada, for instance, and I search for it in the United Kingdom, how long will I take to find it? This paper argues that problems in education are caused by non‐professional teachers who are employed when trained teachers move in search of promotion friendly activities or financially rewarding duties. This shift of focus means that policy makers in education act without adequate professional guidance. The problems in education, therefore, result from demands made on mainstream (...) education based on misconceptions about what education can offer. It is argued that the implementation of e‐learning in education faces the risk of developing on the basis of unproven theories. This scenario increasingly sees the replacement of formal education activities in institutions of learning with non‐formal and informal education practices. Given that the contents and influences of non‐formal and informal education are not under the control of the teacher, the experiences that learners bring to education settings are increasingly difficult to manage. The paper proposes that by integrating e‐learning in teacher education and rewarding ‘good teaching’, there is a potential for a successful e‐learning revolution in education. (shrink)

Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- (...) and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries. (shrink)

The Professional Practice Program, also known as the co-operative education (co-op) program, at the University of Cincinnati (UC) is designed to provide eligible students with the most comprehensive and professional preparation available. Beginning with the Class of 2006, students in UC’s Centennial Co-op Class will be following a new co-op curriculum centered around a set of learning outcomes Regardless of their particular discipline, students will pursue common learning outcomes by participating in the Professional Practice Program, which will cover (...) issues of organizational culture, technology, professional ethics, and the integration of theory and practice. During their third co-op work term, students will complete a learning module on Professional Ethics. To complete the learning module students must familiarize themselves with the code of ethics for their profession, create a hypothetical scenario portraying an ethical dilemma that involves issues covered by the code, resolve the dilemma, and explain why their resolution is the best course of action based upon the code of ethics. A three-party assessment process including students, employers and faculty complete the module. (shrink)

This paper deals with EMIL-S, a software tool box which was designed during the EMIL project for the simulation of processes during which norms emerged in an agent society. This tool box implements the cognitive architecture of normative agents which was designed during the EMIL project which is also discussed in other papers in this issue. This implementation is described in necessary detail, and two examples of its application to several different scenarios are given, namely a scenario in which (...) persons involved in micro finance are simulated and learn how to sanction free riders and how to learn from these sanctions, and a scenario in which simulated persons move through a simulated airport where they more often than not have to wait in queues and learn how to behave properly in queues. (shrink)

Surprise has been explored as a cognitive-emotional phenomenon that impacts many aspects of mental life from creativity to learning to decision-making. In this paper, we specifically address the role of surprise in learning and memory. Although surprise has been cast as a basic emotion since Darwin's (1872) The Expression of the Emotions in Man and Animals, recently more emphasis has been placed on its cognitive aspects. One such view casts surprise as a process of “sense making” or “explanation (...) finding”: metacognitive explanation-based theory proposes that people's perception of surprise is a metacognitive assessment of the cognitive work done to explain a surprising outcome. Or, to put it more simply, surprise increases with the explanatory work required to resolve it. This theory predicts that some surprises should be more surprising than others because they are harder to explain. In the current paper, this theory is extended to consider the role of surprise in learning as evidenced by memorability. This theory is tested to determine how scenarios with differentially surprising outcomes impact the memorability of those outcomes. The results show that surprising outcomes (less-known outcomes) that are more difficult to explain are recalled more accurately than less-surprising outcomes that require little (known outcomes) or no explanation (normal). (shrink)

If I lose my key in Canada, for instance, and I search for it in the United Kingdom, how long will I take to find it? This paper argues that problems in education are caused by non-professional teachers who are employed when trained teachers move in search of promotion friendly activities or financially rewarding duties. This shift of focus means that policy makers in education act without adequate professional guidance. The problems in education, therefore, result from demands made on mainstream (...) education based on misconceptions about what education can offer. It is argued that the implementation of e-learning in education faces the risk of developing on the basis of unproven theories. This scenario increasingly sees the replacement of formal education activities in institutions of learning with non-formal and informal education practices. Given that the contents and influences of non-formal and informal education are not under the control of the teacher, the experiences that learners bring to education settings are increasingly difficult to manage. The paper proposes that by integrating e-learning in teacher education and rewarding 'good teaching', there is a potential for a successful e-learning revolution in education. (shrink)

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense mission planning is described as a sequential decision-making problem and formalized as a Markov decision (...) process. Then, the SEAD intelligent planning model based on the proximal policy optimization algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy. (shrink)

[This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell (...) explanation for student learning in general. I argue that identifying the person with the brain is scientism (not science), that the brain is not the person, and that it is the person who learns. In fact the brain only responds to the learning of embodied experience within the extra-neural network of intersubjective communications. Learning is a dynamic, cultural activity, not a neural program. Brain-based learning is unnecessary for educators and may be dangerous in that a culturally narrow ontology is taken for granted, thus restricting our creativity and imagination, and narrowing the human community. (shrink)

Professional work is currently based on explicit knowledge and evidence to a greater degree than in the past. Standardising professional services in this way requires repetitive scenarios and might be seen as a challenge to professional autonomy. In the context of policing, officers perform a range of familiar tasks, but they may also encounter novel challenges at any moment. Moreover, police tasks are not well-defined. Therefore, many missions require police officers to rely on common sense, tacit knowledge or gut (...) feeling. In this article, I argue that a values-based methodology may serve as a tool to help evaluate decisions in unfamiliar situations, to learn from experience, as well as be a quality control for established routines. _Keywords:_ ethics, policing, decision-making, values, experiential learning routines. (shrink)

Given the widespread acceptance of computational thinking in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things combines artificial intelligence and the Internet of things and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (...) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design. (shrink)

In the last decades neuroscience provided so many important contributions to philosophy of mind that nowadays the latter is inconceivable without the former in every topic this philosophical branch deals with. The studies connected to action understanding provided great advances in the field of developmental psychology for what concerns social learning abilities grounded on imitation. All information received by the infants are transmitted through actions. It would be impossible to conceive infant imitation without action interpretation. According to Meltzoff’s “like-me” (...) hypothesis, imitation is possible in human infants already at birth in virtue of an identification mechanism with the adults supported by mirror neurons (MNs) based simulation system. However, if we split the types of actions into two general categories, instrumental and communicative actions, we will see, according to an alternative account, how infants modulate differently the comprehension of observed scenarios, depending on whether they are passive observers (in the case of instrumental actions) or actively involved (in the case of communicative actions). Such a recognition of action features seems to be evident through different degrees of motor activation, as ERP techniques applied to infants and young adults revealed. Neuroscientific evidence highlight the crucial role of brain areas connected to motor activation for action interpretation, but at the same time, they allow both a bottom-up process and a top-down process interpretation whereby the motor activation is seen as a product of action understanding rather than its determining causal factor. The aim of the present study is to examine such epistemological conflicting perspectives underlying action interpretation, and their repercussions on different social learning theories. (shrink)

Why do societies collapse? We use an individual-based evolutionary model to show that, in environmental conditions dominated by low-frequency variation (“red noise”), extirpation may be an outcome of the evolution of cultural capacity. Previous analytical models predicted an equilibrium between individual learners and social learners, or a contingent strategy in which individuals learn socially or individually depending on the circumstances. However, in red noise environments, whose main signature is that variation is concentrated in relatively large, relatively rare excursions, individual (...) learning may be selected from the population. If the social learning system comes to lack sufficient individual learning or cognitively costly adaptive biases, behavior ceases tracking environmental variation. Then, when the environment does change, fitness declines and the population may collapse or even be extirpated. The modeled scenario broadly fits some human population collapses and might also explain nonhuman extirpations. Varying model parameters showed that the fixation of social learning is less likely when individual learning is less costly, when the environment is less red or more variable, with larger population sizes, and when learning is not conformist or is from parents rather than from the general population. Once social learning is fixed, extirpation is likely except when social learning is biased towards successful models. Thus, the risk of population collapse may be reduced by promoting individual learning and innovation over cultural conformity, or by preferential selection of relatively fit individuals as models for social learning. © 2009 Elsevier Inc. All rights reserved. (shrink)

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according (...) to its similarity to past instances, adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. (shrink)